{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]\n",
    "gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa1508>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(years, gdp, color='green',  marker='o',  linestyle='solid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 17 ms\n"
     ]
    }
   ],
   "source": [
    "my_arr = np. arange( 1000000)\n",
    "my_list = list( range( 1000000) )\n",
    "%time for _ in range( 10) : my_arr2 = my_arr * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
